Optimal control of a mild hybrid electric vehicle using weight functions and genetic algorithms

As a viable alternative to the conventional hybrid electric vehicles, so called "mild" hybrid drivetrains are currently being implemented in production vehicles. These mild hybrid electric vehicles use an Integrated Starter Generator (ISG) to simply assist the internal combustion (IC) engine rather than drive the vehicle independently of the IC engine. Some production mild hybrid vehicles have been shown to achieve over a 10 % increase in fuel efficiency with minimal additional costs compared to a conventional vehicle. In this thesis, lookup table-based control schemes for the optimal control of the ISG and the IC engine on a mild hybrid vehicle are presented. The lookup table is optimized using two different methods: a traditional weighted cost function approach and a genetic algorithm optimization approach. The developed control logic is implemented in Matlab/Simulink along with a mild hybrid vehicle model, which is based on the EPA's light-duty vehicle model.;The simulation results show that the ISG control lookup table constructed by using a weighted cost function yields better performance than a standard rule-based control strategy. In addition, the lookup table is easily implementable for real-time control on the control hardware in current production vehicles. However, it was suspected that the optimization of the ISG control lookup table for an MHEV may require a method designed to handle more complex nonlinear problems than can be optimized by a weighted cost function. Thus, a widely-used nonlinear global optimization technique called genetic algorithm was utilized for further optimization of the ISG control lookup table. The genetic algorithm forms an optimal table with better performance characteristics than the weighted cost function method, which suggests that genetic algorithm optimization is a good choice for the optimization of HEV lookup tables.